Population genomic screening of all young adults in a health-care system: a cost-effectiveness analysis

Abstract

Purpose: To consider the impact and cost-effectiveness of offering preventive population genomic screening to all young adults in a single-payer health-care system.

Methods: We modeled screening of 2,688,192 individuals, all adults aged 18-25 years in Australia, for pathogenic variants in BRCA1/BRCA2/MLH1/MSH2 genes, and carrier screening for cystic fibrosis (CF), spinal muscular atrophy (SMA), and fragile X syndrome (FXS), at 71% testing uptake using per-test costs ranging from AUD$200 to $1200 (~USD$140 to $850). Investment costs included genetic counseling, surveillance, and interventions (reimbursed only) for at-risk individuals/couples. Cost-effectiveness was defined below AUD$50,000/DALY (disability-adjusted life year) prevented, using an incremental cost-effectiveness ratio (ICER), compared with current targeted testing. Outcomes were cancer incidence/mortality, disease cases, and treatment costs reduced.

Results: Population screening would reduce variant-attributable cancers by 28.8%, cancer deaths by 31.2%, and CF/SMA/FXS cases by 24.8%, compared with targeted testing. Assuming AUD$400 per test, investment required would be between 4 and 5 times higher than current expenditure. However, screening would lead to substantial savings in medical costs and DALYs prevented, at a highly cost-effective ICER of AUD$4038/DALY. At AUD$200 per test, screening would approach cost-saving for the health system (ICER = AUD$22/DALY).

Conclusion: Preventive genomic screening in early adulthood would be highly cost-effective in a single-payer health-care system, but ethical issues must be considered.

Keywords: cancer; cost-effectiveness analysis; population genomic screening; preconception carrier screening; prevention.

Fig. 1

Schematic: Preventive genomic screening of all young adults in a single-payer health-care system. The model is purposely conservative, based on the Australian health system, estimating the total preventive health impact and cost-effectiveness of offering combined cancer gene testing and preconception carrier screening concurrently to all adults aged 18–25 years.

Fig. 2

Population genomic screening: decision-analytic tree models. Seven independent decision-analytic tree models were constructed. Each forecasts the cost-effectiveness and preventive impact of population genomic screening for a given disease, compared with current targeted/criteria-based testing. Models focused on either adult cancer gene testing (S2.1–2.4) or preconception carrier screening (S2.5–2.7). We forecast changes in health system costs and disability-adjusted life years (DALYs) prevented as a result of population genomic screening, compared with targeted testing. Cost-effectiveness was calculated from a health system payer perspective using incremental cost-effectiveness ratio (ICER = Δcosts/ΔDALYs) for each independent model, then combined for all models. For detailed methods and tree model diagrams see Supplementary Materials S2.1–2.7. CF cystic fibrosis, FXS fragile X syndrome, SMA spinal muscular atrophy.

Fig. 3

Preventive genomic screening of young adults becomes increasingly cost-effective as more conditions are screened for concurrently. Probability sensitivity analysis was used to calculate the cost-effectiveness of preventive genomic screening in early adulthood at a fixed per-test cost of AUD$400, versus targeted testing. Population cancer gene testing was modeled for four familial cancers—breast, ovarian, colorectal, and endometrial—attributable to BRCA1, BRCA2, MLH1, and MSH2 variants, plus preconception carrier screening (PCS) for three severe rare genetic conditions (cystic fibrosis [CF], spinal muscular atrophy [SMA], and fragile X syndrome [FXS]). Cost-effectiveness was calculated for each condition independently and then in combinations. Y-axis shows differences in investment (cost) in $AUDM, and X-axis shows changes in disability-adjusted life years (DALYs). Cost-effectiveness is represented as the incremental cost-effectiveness ratio (ICER), or cost/DALY prevented. ICER under the willingness-to-pay threshold of AUD$50,000/DALY is considered cost-effective. Models were run iteratively using 2000 simulations for each scenario, with ±25% uncertainty ranges, each iteration represented as a separate dot. (a) Independent population screening for each of the seven conditions. (b) Combined level 1: population cancer gene testing (breast and ovarian, Lynch syndrome); and population PCS (CF + SMA + FXS) separately. (c) Combined level 2: population testing for all cancer genes together (BRCA1/2, MLH1, MSH2); and population PCS (CF + SMA + FXS). (d) Combined level 3: concurrent population genomic screening for all seven conditions combined.

Fig. 4

Preventive genomic screening of young adults at a per-test cost of AUD$200 would potentially be cost-saving to the health system. Probability sensitivity analysis was used to calculate the cost-effectiveness of preventive genomic screening in early adulthood for seven conditions concurrently, at a per-test cost of AUD$200, versus targeted testing. Y-axis shows differences in investment (cost) in $AUDM, and X-axis shows changes in disability-adjusted life years (DALYs). Cost-effectiveness is represented as incremental cost-effectiveness ratio (ICER), or cost/DALY gained. ICER under the willingness-to-pay threshold of AUD$50,000/DALY is considered cost-effective; under AUD$0/DALY is considered cost-saving. Models were run iteratively using 2000 simulations for each scenario, with ±25% uncertainty ranges, with each iteration represented as a separate dot.